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Laboratory of Structural Methods of Data Analysis in Predictive Modeling

Contract number
11.G34.31.0073
Time span of the project
2011-2015

As of 30.01.2020

15
Number of staff members
32
scientific publications
2
Objects of intellectual property
General information

Name of the project: Methods of structural data analysis in predictive modeling

Strategy for Scientific and Technological Development Priority Level: a


Goals and objectives

Research directions: Methods of optimization and statistics

Project objective: Development of new methods of optimization for high-dimensional spaces. Development of new methods of statistical estimation for high-dimensional spaces with presence of noise signals. Development of new methods of stochastic analysis. Applications of developed methods to problems of medicine, finance, engineering etc.


The practical value of the study

The Laboratory has proposed:

  • a robust principal component method;
  • a method of statistical estimation finite samples when noise and error are present in the model specification;
  • a new boosting algorithms;
  • a universal gradient method for problems of convex optimization;
  • new methods of stochastic analysis and numerical modeling of stochastic processes. 

Implemented results of research: Proposed methods and approaches are actively used in solving problems in machine learning (predictive modeling, parameter evaluation), financial mathematics (evaluation of derivatives), transportation modeling, telecommunications and inn other fields.

Education and career development:

  • Members of the academic staff of the Laboratory have provided training in statistics and optimization.
  • The Laboratory has conducted conferences, summer schools, mini-course for students with participation of leading foreign scientists.

Collaborations: Joseph Fourier University (France), Weierstrass Institute (Germany), Center for Operations Research and Econometric(Belgium): joint research

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Gach F., Nickl R., Spokoiny V.
Spatially adaptive density estimation by localised haar projections. Annales de l'institut Henri Poincaré Probabilités et Statistiques 49(3): (2011).
Diederichs E., Juditsky A., Nemirovskii A., Spokoiny V.
Sparse nongaussian component analysis by semidefinite programming. Machine Learning 91(2): (2011).
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